Overview

Dataset statistics

Number of variables24
Number of observations12694445
Missing cells96336624
Missing cells (%)31.6%
Duplicate rows32022
Duplicate rows (%)0.3%
Total size in memory9.3 GiB
Average record size in memory786.0 B

Variable types

Categorical10
Numeric13
Unsupported1

Alerts

Dataset has 32022 (0.3%) duplicate rowsDuplicates
ImportDate has a high cardinality: 266 distinct valuesHigh cardinality
FederalTIN has a high cardinality: 335 distinct valuesHigh cardinality
PlanId has a high cardinality: 16808 distinct valuesHigh cardinality
RatingAreaId has a high cardinality: 67 distinct valuesHigh cardinality
IssuerId is highly overall correlated with IssuerId2High correlation
IssuerId2 is highly overall correlated with IssuerIdHigh correlation
IndividualRate is highly overall correlated with IndividualTobaccoRate and 7 other fieldsHigh correlation
IndividualTobaccoRate is highly overall correlated with IndividualRateHigh correlation
Couple is highly overall correlated with IndividualRate and 7 other fieldsHigh correlation
PrimarySubscriberAndOneDependent is highly overall correlated with IndividualRate and 7 other fieldsHigh correlation
PrimarySubscriberAndTwoDependents is highly overall correlated with IndividualRate and 7 other fieldsHigh correlation
PrimarySubscriberAndThreeOrMoreDependents is highly overall correlated with IndividualRate and 7 other fieldsHigh correlation
CoupleAndOneDependent is highly overall correlated with IndividualRate and 7 other fieldsHigh correlation
CoupleAndTwoDependents is highly overall correlated with IndividualRate and 7 other fieldsHigh correlation
CoupleAndThreeOrMoreDependents is highly overall correlated with IndividualRate and 7 other fieldsHigh correlation
BusinessYear is highly overall correlated with RateEffectiveDate and 1 other fieldsHigh correlation
StateCode is highly overall correlated with SourceNameHigh correlation
SourceName is highly overall correlated with StateCodeHigh correlation
RateEffectiveDate is highly overall correlated with BusinessYear and 1 other fieldsHigh correlation
RateExpirationDate is highly overall correlated with BusinessYear and 1 other fieldsHigh correlation
Tobacco is highly overall correlated with Couple and 6 other fieldsHigh correlation
IndividualTobaccoRate has 7762096 (61.1%) missing valuesMissing
Couple has 12653504 (99.7%) missing valuesMissing
PrimarySubscriberAndOneDependent has 12653504 (99.7%) missing valuesMissing
PrimarySubscriberAndTwoDependents has 12653504 (99.7%) missing valuesMissing
PrimarySubscriberAndThreeOrMoreDependents has 12653504 (99.7%) missing valuesMissing
CoupleAndOneDependent has 12653504 (99.7%) missing valuesMissing
CoupleAndTwoDependents has 12653504 (99.7%) missing valuesMissing
CoupleAndThreeOrMoreDependents has 12653504 (99.7%) missing valuesMissing
Age is an unsupported type, check if it needs cleaning or further analysisUnsupported
IndividualRate has 682484 (5.4%) zerosZeros

Reproduction

Analysis started2023-03-01 22:46:46.025028
Analysis finished2023-03-01 22:55:34.586002
Duration8 minutes and 48.56 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

BusinessYear
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size738.5 MiB
2015
4676092 
2016
4221965 
2014
3796388 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters50777780
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2014
5th row2014

Common Values

ValueCountFrequency (%)
2015 4676092
36.8%
2016 4221965
33.3%
2014 3796388
29.9%

Length

2023-03-01T17:55:34.862715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-01T17:55:35.039414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2015 4676092
36.8%
2016 4221965
33.3%
2014 3796388
29.9%

Most occurring characters

ValueCountFrequency (%)
2 12694445
25.0%
0 12694445
25.0%
1 12694445
25.0%
5 4676092
 
9.2%
6 4221965
 
8.3%
4 3796388
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50777780
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 12694445
25.0%
0 12694445
25.0%
1 12694445
25.0%
5 4676092
 
9.2%
6 4221965
 
8.3%
4 3796388
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 50777780
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 12694445
25.0%
0 12694445
25.0%
1 12694445
25.0%
5 4676092
 
9.2%
6 4221965
 
8.3%
4 3796388
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50777780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 12694445
25.0%
0 12694445
25.0%
1 12694445
25.0%
5 4676092
 
9.2%
6 4221965
 
8.3%
4 3796388
 
7.5%

StateCode
Categorical

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size714.3 MiB
FL
1702472 
SC
1563770 
MI
1023190 
WI
1013278 
OH
884530 
Other values (34)
6507205 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters25388890
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAK
2nd rowAK
3rd rowAK
4th rowAK
5th rowAK

Common Values

ValueCountFrequency (%)
FL 1702472
13.4%
SC 1563770
12.3%
MI 1023190
 
8.1%
WI 1013278
 
8.0%
OH 884530
 
7.0%
TX 859714
 
6.8%
IN 677393
 
5.3%
PA 475022
 
3.7%
GA 472012
 
3.7%
IL 432827
 
3.4%
Other values (29) 3590237
28.3%

Length

2023-03-01T17:55:35.156774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fl 1702472
13.4%
sc 1563770
12.3%
mi 1023190
 
8.1%
wi 1013278
 
8.0%
oh 884530
 
7.0%
tx 859714
 
6.8%
in 677393
 
5.3%
pa 475022
 
3.7%
ga 472012
 
3.7%
il 432827
 
3.4%
Other values (29) 3590237
28.3%

Most occurring characters

ValueCountFrequency (%)
I 3388258
13.3%
L 2504255
9.9%
A 2331150
 
9.2%
C 1929454
 
7.6%
S 1795756
 
7.1%
F 1702472
 
6.7%
N 1645580
 
6.5%
M 1473836
 
5.8%
O 1288352
 
5.1%
T 1249738
 
4.9%
Other values (14) 6080039
23.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 25388890
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 3388258
13.3%
L 2504255
9.9%
A 2331150
 
9.2%
C 1929454
 
7.6%
S 1795756
 
7.1%
F 1702472
 
6.7%
N 1645580
 
6.5%
M 1473836
 
5.8%
O 1288352
 
5.1%
T 1249738
 
4.9%
Other values (14) 6080039
23.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 25388890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 3388258
13.3%
L 2504255
9.9%
A 2331150
 
9.2%
C 1929454
 
7.6%
S 1795756
 
7.1%
F 1702472
 
6.7%
N 1645580
 
6.5%
M 1473836
 
5.8%
O 1288352
 
5.1%
T 1249738
 
4.9%
Other values (14) 6080039
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25388890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 3388258
13.3%
L 2504255
9.9%
A 2331150
 
9.2%
C 1929454
 
7.6%
S 1795756
 
7.1%
F 1702472
 
6.7%
N 1645580
 
6.5%
M 1473836
 
5.8%
O 1288352
 
5.1%
T 1249738
 
4.9%
Other values (14) 6080039
23.9%

IssuerId
Real number (ℝ)

Distinct910
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52485.924
Minimum10046
Maximum99969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:35.279379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10046
5-th percentile15560
Q130219
median49532
Q376526
95-th percentile97325
Maximum99969
Range89923
Interquartile range (IQR)46307

Descriptive statistics

Standard deviation26412.627
Coefficient of variation (CV)0.50323257
Kurtosis-1.1584024
Mean52485.924
Median Absolute Deviation (MAD)20676
Skewness0.26753761
Sum6.6627968 × 1011
Variance6.9762685 × 108
MonotonicityNot monotonic
2023-03-01T17:55:35.408449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49532 694048
 
5.5%
15560 340968
 
2.7%
33602 287040
 
2.3%
16842 277426
 
2.2%
84670 275172
 
2.2%
50816 258060
 
2.0%
38166 210450
 
1.7%
97325 166152
 
1.3%
65122 162932
 
1.3%
26065 156584
 
1.2%
Other values (900) 9865613
77.7%
ValueCountFrequency (%)
10046 184
 
< 0.1%
10064 68
 
< 0.1%
10091 12144
 
0.1%
10191 31096
0.2%
10204 672
 
< 0.1%
10207 3680
 
< 0.1%
10739 736
 
< 0.1%
11083 26624
0.2%
11103 3212
 
< 0.1%
11245 552
 
< 0.1%
ValueCountFrequency (%)
99969 59708
0.5%
99787 58558
0.5%
99734 16167
 
0.1%
99708 5184
 
< 0.1%
99663 6256
 
< 0.1%
99568 56
 
< 0.1%
99389 5796
 
< 0.1%
99248 4830
 
< 0.1%
99180 9142
 
0.1%
99129 552
 
< 0.1%

SourceName
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size742.0 MiB
HIOS
8702452 
SERFF
3853855 
OPM
 
138138

Length

Max length5
Median length4
Mean length4.2927042
Min length3

Characters and Unicode

Total characters54493497
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHIOS
2nd rowHIOS
3rd rowHIOS
4th rowHIOS
5th rowHIOS

Common Values

ValueCountFrequency (%)
HIOS 8702452
68.6%
SERFF 3853855
30.4%
OPM 138138
 
1.1%

Length

2023-03-01T17:55:35.530589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-01T17:55:35.645420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
hios 8702452
68.6%
serff 3853855
30.4%
opm 138138
 
1.1%

Most occurring characters

ValueCountFrequency (%)
S 12556307
23.0%
O 8840590
16.2%
H 8702452
16.0%
I 8702452
16.0%
F 7707710
14.1%
E 3853855
 
7.1%
R 3853855
 
7.1%
P 138138
 
0.3%
M 138138
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 54493497
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 12556307
23.0%
O 8840590
16.2%
H 8702452
16.0%
I 8702452
16.0%
F 7707710
14.1%
E 3853855
 
7.1%
R 3853855
 
7.1%
P 138138
 
0.3%
M 138138
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 54493497
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 12556307
23.0%
O 8840590
16.2%
H 8702452
16.0%
I 8702452
16.0%
F 7707710
14.1%
E 3853855
 
7.1%
R 3853855
 
7.1%
P 138138
 
0.3%
M 138138
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54493497
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 12556307
23.0%
O 8840590
16.2%
H 8702452
16.0%
I 8702452
16.0%
F 7707710
14.1%
E 3853855
 
7.1%
R 3853855
 
7.1%
P 138138
 
0.3%
M 138138
 
0.3%

VersionNum
Real number (ℝ)

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8655583
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:35.733624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q39
95-th percentile14
Maximum24
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.85718
Coefficient of variation (CV)0.56181592
Kurtosis3.2115208
Mean6.8655583
Median Absolute Deviation (MAD)2
Skewness1.4067974
Sum87154452
Variance14.877837
MonotonicityNot monotonic
2023-03-01T17:55:35.840522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6 1525820
12.0%
4 1506044
11.9%
7 1454216
11.5%
9 1447516
11.4%
8 1327285
10.5%
5 1179399
9.3%
3 1082273
8.5%
2 747516
5.9%
10 667468
5.3%
1 389078
 
3.1%
Other values (13) 1367830
10.8%
ValueCountFrequency (%)
1 389078
 
3.1%
2 747516
5.9%
3 1082273
8.5%
4 1506044
11.9%
5 1179399
9.3%
6 1525820
12.0%
7 1454216
11.5%
8 1327285
10.5%
9 1447516
11.4%
10 667468
5.3%
ValueCountFrequency (%)
24 24840
 
0.2%
23 48254
 
0.4%
21 13110
 
0.1%
20 265466
2.1%
19 17986
 
0.1%
18 10580
 
0.1%
17 2438
 
< 0.1%
16 39514
 
0.3%
15 98900
 
0.8%
14 202308
1.6%

ImportDate
Categorical

Distinct266
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size920.1 MiB
2013-11-26 13:14:08
 
511886
2015-08-22 15:09:32
 
451312
2015-08-26 09:56:12
 
449302
2015-01-16 17:32:32
 
433342
2014-01-17 09:36:20
 
351302
Other values (261)
10497301 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters241194455
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014-03-19 07:06:49
2nd row2014-03-19 07:06:49
3rd row2014-03-19 07:06:49
4th row2014-03-19 07:06:49
5th row2014-03-19 07:06:49

Common Values

ValueCountFrequency (%)
2013-11-26 13:14:08 511886
 
4.0%
2015-08-22 15:09:32 451312
 
3.6%
2015-08-26 09:56:12 449302
 
3.5%
2015-01-16 17:32:32 433342
 
3.4%
2014-01-17 09:36:20 351302
 
2.8%
2014-01-21 08:29:49 312902
 
2.5%
2014-03-19 07:06:49 284962
 
2.2%
2015-10-18 12:35:12 266088
 
2.1%
2015-04-22 11:06:15 250608
 
2.0%
2013-11-27 09:24:34 230908
 
1.8%
Other values (256) 9151833
72.1%

Length

2023-03-01T17:55:35.952341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-08-22 563080
 
2.2%
2013-11-26 511886
 
2.0%
13:14:08 511886
 
2.0%
15:09:32 451312
 
1.8%
2015-01-16 449478
 
1.8%
2015-08-26 449302
 
1.8%
09:56:12 449302
 
1.8%
17:32:32 433342
 
1.7%
2014-01-21 372982
 
1.5%
2014-01-17 363584
 
1.4%
Other values (457) 20832736
82.1%

Most occurring characters

ValueCountFrequency (%)
1 40737378
16.9%
0 38711097
16.0%
2 32770658
13.6%
- 25388890
10.5%
: 25388890
10.5%
5 14513171
 
6.0%
4 14122006
 
5.9%
12694445
 
5.3%
3 11405835
 
4.7%
8 8240114
 
3.4%
Other values (3) 17221971
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177722230
73.7%
Dash Punctuation 25388890
 
10.5%
Other Punctuation 25388890
 
10.5%
Space Separator 12694445
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 40737378
22.9%
0 38711097
21.8%
2 32770658
18.4%
5 14513171
 
8.2%
4 14122006
 
7.9%
3 11405835
 
6.4%
8 8240114
 
4.6%
9 7307904
 
4.1%
6 5500384
 
3.1%
7 4413683
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 25388890
100.0%
Other Punctuation
ValueCountFrequency (%)
: 25388890
100.0%
Space Separator
ValueCountFrequency (%)
12694445
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 241194455
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 40737378
16.9%
0 38711097
16.0%
2 32770658
13.6%
- 25388890
10.5%
: 25388890
10.5%
5 14513171
 
6.0%
4 14122006
 
5.9%
12694445
 
5.3%
3 11405835
 
4.7%
8 8240114
 
3.4%
Other values (3) 17221971
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 241194455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 40737378
16.9%
0 38711097
16.0%
2 32770658
13.6%
- 25388890
10.5%
: 25388890
10.5%
5 14513171
 
6.0%
4 14122006
 
5.9%
12694445
 
5.3%
3 11405835
 
4.7%
8 8240114
 
3.4%
Other values (3) 17221971
7.1%

IssuerId2
Real number (ℝ)

Distinct910
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52485.924
Minimum10046
Maximum99969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:36.084186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10046
5-th percentile15560
Q130219
median49532
Q376526
95-th percentile97325
Maximum99969
Range89923
Interquartile range (IQR)46307

Descriptive statistics

Standard deviation26412.627
Coefficient of variation (CV)0.50323257
Kurtosis-1.1584024
Mean52485.924
Median Absolute Deviation (MAD)20676
Skewness0.26753761
Sum6.6627968 × 1011
Variance6.9762685 × 108
MonotonicityNot monotonic
2023-03-01T17:55:36.224611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49532 694048
 
5.5%
15560 340968
 
2.7%
33602 287040
 
2.3%
16842 277426
 
2.2%
84670 275172
 
2.2%
50816 258060
 
2.0%
38166 210450
 
1.7%
97325 166152
 
1.3%
65122 162932
 
1.3%
26065 156584
 
1.2%
Other values (900) 9865613
77.7%
ValueCountFrequency (%)
10046 184
 
< 0.1%
10064 68
 
< 0.1%
10091 12144
 
0.1%
10191 31096
0.2%
10204 672
 
< 0.1%
10207 3680
 
< 0.1%
10739 736
 
< 0.1%
11083 26624
0.2%
11103 3212
 
< 0.1%
11245 552
 
< 0.1%
ValueCountFrequency (%)
99969 59708
0.5%
99787 58558
0.5%
99734 16167
 
0.1%
99708 5184
 
< 0.1%
99663 6256
 
< 0.1%
99568 56
 
< 0.1%
99389 5796
 
< 0.1%
99248 4830
 
< 0.1%
99180 9142
 
0.1%
99129 552
 
< 0.1%

FederalTIN
Categorical

Distinct335
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.1 MiB
57-0768835
 
694048
47-0397286
 
613192
36-1236610
 
519432
13-5581829
 
425624
95-6042390
 
353843
Other values (330)
10088306 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters126944450
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row93-0438772
2nd row93-0438772
3rd row93-0438772
4th row93-0438772
5th row93-0438772

Common Values

ValueCountFrequency (%)
57-0768835 694048
 
5.5%
47-0397286 613192
 
4.8%
36-1236610 519432
 
4.1%
13-5581829 425624
 
3.4%
95-6042390 353843
 
2.8%
38-2069753 340968
 
2.7%
75-1233841 283820
 
2.2%
59-2015694 277426
 
2.2%
20-2660193 275172
 
2.2%
31-1069321 258060
 
2.0%
Other values (325) 8652860
68.2%

Length

2023-03-01T17:55:36.336197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
57-0768835 694048
 
5.5%
47-0397286 613192
 
4.8%
36-1236610 519432
 
4.1%
13-5581829 425624
 
3.4%
95-6042390 353843
 
2.8%
38-2069753 340968
 
2.7%
75-1233841 283820
 
2.2%
59-2015694 277426
 
2.2%
20-2660193 275172
 
2.2%
31-1069321 258060
 
2.0%
Other values (325) 8652860
68.2%

Most occurring characters

ValueCountFrequency (%)
3 15865123
12.5%
0 12932923
10.2%
- 12694445
10.0%
1 12304992
9.7%
5 12120009
9.5%
2 11659245
9.2%
6 10796362
8.5%
9 10335050
8.1%
8 9958719
7.8%
7 9473461
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 114250005
90.0%
Dash Punctuation 12694445
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 15865123
13.9%
0 12932923
11.3%
1 12304992
10.8%
5 12120009
10.6%
2 11659245
10.2%
6 10796362
9.4%
9 10335050
9.0%
8 9958719
8.7%
7 9473461
8.3%
4 8804121
7.7%
Dash Punctuation
ValueCountFrequency (%)
- 12694445
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 126944450
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 15865123
12.5%
0 12932923
10.2%
- 12694445
10.0%
1 12304992
9.7%
5 12120009
9.5%
2 11659245
9.2%
6 10796362
8.5%
9 10335050
8.1%
8 9958719
7.8%
7 9473461
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126944450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 15865123
12.5%
0 12932923
10.2%
- 12694445
10.0%
1 12304992
9.7%
5 12120009
9.5%
2 11659245
9.2%
6 10796362
8.5%
9 10335050
8.1%
8 9958719
7.8%
7 9473461
7.5%
Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.1 MiB
2015-01-01
2951582 
2016-01-01
2669814 
2014-01-01
2382530 
2015-07-01
583820 
2015-10-01
570848 
Other values (9)
3535851 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters126944450
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014-01-01
2nd row2014-01-01
3rd row2014-01-01
4th row2014-01-01
5th row2014-01-01

Common Values

ValueCountFrequency (%)
2015-01-01 2951582
23.3%
2016-01-01 2669814
21.0%
2014-01-01 2382530
18.8%
2015-07-01 583820
 
4.6%
2015-10-01 570848
 
4.5%
2015-04-01 570848
 
4.5%
2016-07-01 525495
 
4.1%
2016-04-01 512799
 
4.0%
2016-10-01 512799
 
4.0%
2014-07-01 476132
 
3.8%
Other values (4) 937778
 
7.4%

Length

2023-03-01T17:55:36.426287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-01-01 2951582
23.3%
2016-01-01 2669814
21.0%
2014-01-01 2382530
18.8%
2015-07-01 583820
 
4.6%
2015-10-01 570848
 
4.5%
2015-04-01 570848
 
4.5%
2016-07-01 525495
 
4.1%
2016-04-01 512799
 
4.0%
2016-10-01 512799
 
4.0%
2014-07-01 476132
 
3.8%
Other values (4) 937778
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 38084103
30.0%
1 34950787
27.5%
- 25388890
20.0%
2 12694445
 
10.0%
4 5342727
 
4.2%
5 4677098
 
3.7%
6 4220907
 
3.3%
7 1585447
 
1.2%
3 46
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101555560
80.0%
Dash Punctuation 25388890
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38084103
37.5%
1 34950787
34.4%
2 12694445
 
12.5%
4 5342727
 
5.3%
5 4677098
 
4.6%
6 4220907
 
4.2%
7 1585447
 
1.6%
3 46
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 25388890
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 126944450
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38084103
30.0%
1 34950787
27.5%
- 25388890
20.0%
2 12694445
 
10.0%
4 5342727
 
4.2%
5 4677098
 
3.7%
6 4220907
 
3.3%
7 1585447
 
1.2%
3 46
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126944450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38084103
30.0%
1 34950787
27.5%
- 25388890
20.0%
2 12694445
 
10.0%
4 5342727
 
4.2%
5 4677098
 
3.7%
6 4220907
 
3.3%
7 1585447
 
1.2%
3 46
 
< 0.1%
Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.1 MiB
2015-12-31
2877574 
2016-12-31
2666681 
2014-12-31
2341400 
2015-06-30
612064 
2015-03-31
600696 
Other values (21)
3596030 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters126944450
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014-12-31
2nd row2014-12-31
3rd row2014-12-31
4th row2014-12-31
5th row2014-12-31

Common Values

ValueCountFrequency (%)
2015-12-31 2877574
22.7%
2016-12-31 2666681
21.0%
2014-12-31 2341400
18.4%
2015-06-30 612064
 
4.8%
2015-03-31 600696
 
4.7%
2015-09-30 600196
 
4.7%
2016-06-30 525495
 
4.1%
2016-03-31 512804
 
4.0%
2016-09-30 512799
 
4.0%
2014-06-30 441954
 
3.5%
Other values (16) 1002782
 
7.9%

Length

2023-03-01T17:55:36.527194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-12-31 2877574
22.7%
2016-12-31 2666681
21.0%
2014-12-31 2341400
18.4%
2015-06-30 612064
 
4.8%
2015-03-31 600696
 
4.7%
2015-09-30 600196
 
4.7%
2016-06-30 525495
 
4.1%
2016-03-31 512804
 
4.0%
2016-09-30 512799
 
4.0%
2014-06-30 441954
 
3.5%
Other values (16) 1002782
 
7.9%

Most occurring characters

ValueCountFrequency (%)
1 30266307
23.8%
- 25388890
20.0%
0 20724499
16.3%
2 20600234
16.2%
3 14128819
11.1%
6 5866016
 
4.6%
5 4752308
 
3.7%
4 3657888
 
2.9%
9 1547805
 
1.2%
8 8556
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101555560
80.0%
Dash Punctuation 25388890
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30266307
29.8%
0 20724499
20.4%
2 20600234
20.3%
3 14128819
13.9%
6 5866016
 
5.8%
5 4752308
 
4.7%
4 3657888
 
3.6%
9 1547805
 
1.5%
8 8556
 
< 0.1%
7 3128
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 25388890
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 126944450
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30266307
23.8%
- 25388890
20.0%
0 20724499
16.3%
2 20600234
16.2%
3 14128819
11.1%
6 5866016
 
4.6%
5 4752308
 
3.7%
4 3657888
 
2.9%
9 1547805
 
1.2%
8 8556
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126944450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30266307
23.8%
- 25388890
20.0%
0 20724499
16.3%
2 20600234
16.2%
3 14128819
11.1%
6 5866016
 
4.6%
5 4752308
 
3.7%
4 3657888
 
2.9%
9 1547805
 
1.2%
8 8556
 
< 0.1%

PlanId
Categorical

Distinct16808
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size859.6 MiB
16842FL0010001
 
36984
16842FL0010002
 
36984
97325SC0080001
 
25392
26065SC0360001
 
25392
84966SC0120001
 
25392
Other values (16803)
12544301 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters177722230
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st row21989AK0010001
2nd row21989AK0020001
3rd row21989AK0020001
4th row21989AK0010001
5th row21989AK0010001

Common Values

ValueCountFrequency (%)
16842FL0010001 36984
 
0.3%
16842FL0010002 36984
 
0.3%
97325SC0080001 25392
 
0.2%
26065SC0360001 25392
 
0.2%
84966SC0120001 25392
 
0.2%
97325SC0080004 25392
 
0.2%
97325SC0080003 25392
 
0.2%
97325SC0080002 25392
 
0.2%
49532SC0370006 25392
 
0.2%
26065SC0360002 25392
 
0.2%
Other values (16798) 12417341
97.8%

Length

2023-03-01T17:55:36.642684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16842fl0010001 36984
 
0.3%
16842fl0010002 36984
 
0.3%
97325sc0080001 25392
 
0.2%
26065sc0360001 25392
 
0.2%
84966sc0120001 25392
 
0.2%
97325sc0080004 25392
 
0.2%
97325sc0080003 25392
 
0.2%
97325sc0080002 25392
 
0.2%
49532sc0370006 25392
 
0.2%
26065sc0360002 25392
 
0.2%
Other values (16795) 12417341
97.8%

Most occurring characters

ValueCountFrequency (%)
0 57664747
32.4%
1 16869837
 
9.5%
2 13243952
 
7.5%
3 11973530
 
6.7%
5 9792709
 
5.5%
6 9475625
 
5.3%
4 9076191
 
5.1%
8 8206352
 
4.6%
7 8122312
 
4.6%
9 7908085
 
4.4%
Other values (26) 25388890
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 152333340
85.7%
Uppercase Letter 25388338
 
14.3%
Lowercase Letter 552
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 3388258
13.3%
L 2504255
9.9%
A 2331150
 
9.2%
C 1929454
 
7.6%
S 1795756
 
7.1%
F 1702472
 
6.7%
N 1645580
 
6.5%
M 1473836
 
5.8%
O 1288352
 
5.1%
T 1249462
 
4.9%
Other values (14) 6079763
23.9%
Decimal Number
ValueCountFrequency (%)
0 57664747
37.9%
1 16869837
 
11.1%
2 13243952
 
8.7%
3 11973530
 
7.9%
5 9792709
 
6.4%
6 9475625
 
6.2%
4 9076191
 
6.0%
8 8206352
 
5.4%
7 8122312
 
5.3%
9 7908085
 
5.2%
Lowercase Letter
ValueCountFrequency (%)
t 276
50.0%
x 276
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 152333340
85.7%
Latin 25388890
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 3388258
13.3%
L 2504255
9.9%
A 2331150
 
9.2%
C 1929454
 
7.6%
S 1795756
 
7.1%
F 1702472
 
6.7%
N 1645580
 
6.5%
M 1473836
 
5.8%
O 1288352
 
5.1%
T 1249462
 
4.9%
Other values (16) 6080315
23.9%
Common
ValueCountFrequency (%)
0 57664747
37.9%
1 16869837
 
11.1%
2 13243952
 
8.7%
3 11973530
 
7.9%
5 9792709
 
6.4%
6 9475625
 
6.2%
4 9076191
 
6.0%
8 8206352
 
5.4%
7 8122312
 
5.3%
9 7908085
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177722230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 57664747
32.4%
1 16869837
 
9.5%
2 13243952
 
7.5%
3 11973530
 
6.7%
5 9792709
 
5.5%
6 9475625
 
5.3%
4 9076191
 
5.1%
8 8206352
 
4.6%
7 8122312
 
4.6%
9 7908085
 
4.4%
Other values (26) 25388890
14.3%

RatingAreaId
Categorical

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size852.8 MiB
Rating Area 1
978906 
Rating Area 2
934953 
Rating Area 3
920486 
Rating Area 4
907736 
Rating Area 5
823124 
Other values (62)
8129240 

Length

Max length14
Median length13
Mean length13.44352
Min length13

Characters and Unicode

Total characters170658023
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRating Area 1
2nd rowRating Area 1
3rd rowRating Area 2
4th rowRating Area 1
5th rowRating Area 1

Common Values

ValueCountFrequency (%)
Rating Area 1 978906
 
7.7%
Rating Area 2 934953
 
7.4%
Rating Area 3 920486
 
7.3%
Rating Area 4 907736
 
7.2%
Rating Area 5 823124
 
6.5%
Rating Area 6 777974
 
6.1%
Rating Area 7 689508
 
5.4%
Rating Area 8 560491
 
4.4%
Rating Area 10 483493
 
3.8%
Rating Area 11 471504
 
3.7%
Other values (57) 5146270
40.5%

Length

2023-03-01T17:55:36.742423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rating 12694445
33.3%
area 12694445
33.3%
1 978906
 
2.6%
2 934953
 
2.5%
3 920486
 
2.4%
4 907736
 
2.4%
5 823124
 
2.2%
6 777974
 
2.0%
7 689508
 
1.8%
8 560491
 
1.5%
Other values (59) 6101267
16.0%

Most occurring characters

ValueCountFrequency (%)
a 25388890
14.9%
25388890
14.9%
R 12694445
7.4%
t 12694445
7.4%
i 12694445
7.4%
n 12694445
7.4%
g 12694445
7.4%
A 12694445
7.4%
r 12694445
7.4%
e 12694445
7.4%
Other values (10) 18324683
10.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101555560
59.5%
Space Separator 25388890
 
14.9%
Uppercase Letter 25388890
 
14.9%
Decimal Number 18324683
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5009239
27.3%
2 2365310
12.9%
3 2119916
11.6%
4 2072355
11.3%
5 1747129
 
9.5%
6 1616618
 
8.8%
7 1106220
 
6.0%
8 803121
 
4.4%
0 777004
 
4.2%
9 707771
 
3.9%
Lowercase Letter
ValueCountFrequency (%)
a 25388890
25.0%
t 12694445
12.5%
i 12694445
12.5%
n 12694445
12.5%
g 12694445
12.5%
r 12694445
12.5%
e 12694445
12.5%
Uppercase Letter
ValueCountFrequency (%)
R 12694445
50.0%
A 12694445
50.0%
Space Separator
ValueCountFrequency (%)
25388890
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 126944450
74.4%
Common 43713573
 
25.6%

Most frequent character per script

Common
ValueCountFrequency (%)
25388890
58.1%
1 5009239
 
11.5%
2 2365310
 
5.4%
3 2119916
 
4.8%
4 2072355
 
4.7%
5 1747129
 
4.0%
6 1616618
 
3.7%
7 1106220
 
2.5%
8 803121
 
1.8%
0 777004
 
1.8%
Latin
ValueCountFrequency (%)
a 25388890
20.0%
R 12694445
10.0%
t 12694445
10.0%
i 12694445
10.0%
n 12694445
10.0%
g 12694445
10.0%
A 12694445
10.0%
r 12694445
10.0%
e 12694445
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170658023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 25388890
14.9%
25388890
14.9%
R 12694445
7.4%
t 12694445
7.4%
i 12694445
7.4%
n 12694445
7.4%
g 12694445
7.4%
A 12694445
7.4%
r 12694445
7.4%
e 12694445
7.4%
Other values (10) 18324683
10.7%

Tobacco
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size922.1 MiB
No Preference
7804323 
Tobacco User/Non-Tobacco User
4890122 

Length

Max length29
Median length13
Mean length19.16348
Min length13

Characters and Unicode

Total characters243269737
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Preference
2nd rowNo Preference
3rd rowNo Preference
4th rowNo Preference
5th rowNo Preference

Common Values

ValueCountFrequency (%)
No Preference 7804323
61.5%
Tobacco User/Non-Tobacco User 4890122
38.5%

Length

2023-03-01T17:55:36.830648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-01T17:55:36.934750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
no 7804323
25.8%
preference 7804323
25.8%
tobacco 4890122
16.2%
user/non-tobacco 4890122
16.2%
user 4890122
16.2%

Most occurring characters

ValueCountFrequency (%)
e 40997536
16.9%
o 32254933
13.3%
c 27364811
11.2%
r 25388890
10.4%
17584567
 
7.2%
N 12694445
 
5.2%
n 12694445
 
5.2%
T 9780244
 
4.0%
b 9780244
 
4.0%
a 9780244
 
4.0%
Other values (6) 44949378
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 175845670
72.3%
Uppercase Letter 40059256
 
16.5%
Space Separator 17584567
 
7.2%
Other Punctuation 4890122
 
2.0%
Dash Punctuation 4890122
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 40997536
23.3%
o 32254933
18.3%
c 27364811
15.6%
r 25388890
14.4%
n 12694445
 
7.2%
b 9780244
 
5.6%
a 9780244
 
5.6%
s 9780244
 
5.6%
f 7804323
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
N 12694445
31.7%
T 9780244
24.4%
U 9780244
24.4%
P 7804323
19.5%
Space Separator
ValueCountFrequency (%)
17584567
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 4890122
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4890122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 215904926
88.8%
Common 27364811
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 40997536
19.0%
o 32254933
14.9%
c 27364811
12.7%
r 25388890
11.8%
N 12694445
 
5.9%
n 12694445
 
5.9%
T 9780244
 
4.5%
b 9780244
 
4.5%
a 9780244
 
4.5%
U 9780244
 
4.5%
Other values (3) 25388890
11.8%
Common
ValueCountFrequency (%)
17584567
64.3%
/ 4890122
 
17.9%
- 4890122
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243269737
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 40997536
16.9%
o 32254933
13.3%
c 27364811
11.2%
r 25388890
10.4%
17584567
 
7.2%
N 12694445
 
5.2%
n 12694445
 
5.2%
T 9780244
 
4.0%
b 9780244
 
4.0%
a 9780244
 
4.0%
Other values (6) 44949378
18.5%

Age
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size715.3 MiB

IndividualRate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct149181
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4098.0265
Minimum0
Maximum999999
Zeros682484
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:37.061347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129.33
median291.6
Q3478.98
95-th percentile948.39
Maximum999999
Range999999
Interquartile range (IQR)449.65

Descriptive statistics

Standard deviation61222.713
Coefficient of variation (CV)14.93956
Kurtosis260.59546
Mean4098.0265
Median Absolute Deviation (MAD)257.42
Skewness16.204209
Sum5.2022171 × 1010
Variance3.7482205 × 109
MonotonicityNot monotonic
2023-03-01T17:55:39.687539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 682484
 
5.4%
99.99 87941
 
0.7%
999999 47790
 
0.4%
99 36455
 
0.3%
1.5 24660
 
0.2%
9999 23580
 
0.2%
18.49 15032
 
0.1%
25.68 13499
 
0.1%
21.99 13179
 
0.1%
36.3 11420
 
0.1%
Other values (149171) 11738405
92.5%
ValueCountFrequency (%)
0 682484
5.4%
0.01 24
 
< 0.1%
1.5 24660
 
0.2%
3.93 322
 
< 0.1%
5.55 1170
 
< 0.1%
6 6
 
< 0.1%
6.12 270
 
< 0.1%
6.6 2996
 
< 0.1%
6.68 1170
 
< 0.1%
7 12
 
< 0.1%
ValueCountFrequency (%)
999999 47790
0.4%
9999.99 1080
 
< 0.1%
9999 23580
0.2%
5503.85 2
 
< 0.1%
5415.8 1
 
< 0.1%
5354.51 2
 
< 0.1%
5319.89 2
 
< 0.1%
5270.86 1
 
< 0.1%
5268.84 1
 
< 0.1%
5259.14 2
 
< 0.1%

IndividualTobaccoRate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct153930
Distinct (%)3.1%
Missing7762096
Missing (%)61.1%
Infinite0
Infinite (%)0.0%
Mean543.69108
Minimum41.73
Maximum6604.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:39.845137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum41.73
5-th percentile220.5
Q1339.12
median463.29
Q3684.39
95-th percentile1104.01
Maximum6604.61
Range6562.88
Interquartile range (IQR)345.27

Descriptive statistics

Standard deviation294.59158
Coefficient of variation (CV)0.54183634
Kurtosis17.612482
Mean543.69108
Median Absolute Deviation (MAD)151.89
Skewness2.286002
Sum2.6816742 × 109
Variance86784.2
MonotonicityNot monotonic
2023-03-01T17:55:39.959187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306 381
 
< 0.1%
312 368
 
< 0.1%
369 353
 
< 0.1%
282 352
 
< 0.1%
252 351
 
< 0.1%
300 348
 
< 0.1%
295 342
 
< 0.1%
330 335
 
< 0.1%
347 333
 
< 0.1%
318 327
 
< 0.1%
Other values (153920) 4928859
38.8%
(Missing) 7762096
61.1%
ValueCountFrequency (%)
41.73 1
< 0.1%
42.64 1
< 0.1%
44.45 1
< 0.1%
55.66 1
< 0.1%
58.88 1
< 0.1%
59.4 1
< 0.1%
60.54 1
< 0.1%
60.59 1
< 0.1%
60.78 1
< 0.1%
60.8 1
< 0.1%
ValueCountFrequency (%)
6604.61 2
< 0.1%
6498.95 1
 
< 0.1%
6425.42 2
< 0.1%
6383.87 2
< 0.1%
6325.03 1
 
< 0.1%
6322.61 1
 
< 0.1%
6310.97 2
< 0.1%
6306.2 2
< 0.1%
6305.81 4
< 0.1%
6291.89 2
< 0.1%

Couple
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5533
Distinct (%)13.5%
Missing12653504
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean48.115714
Minimum0
Maximum182.4
Zeros6302
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:40.081797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126.71
median49.77
Q369.9
95-th percentile97.06
Maximum182.4
Range182.4
Interquartile range (IQR)43.19

Descriptive statistics

Standard deviation30.422891
Coefficient of variation (CV)0.63228597
Kurtosis-0.7229547
Mean48.115714
Median Absolute Deviation (MAD)22.09
Skewness0.044568678
Sum1969905.5
Variance925.5523
MonotonicityNot monotonic
2023-03-01T17:55:40.190717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6302
 
< 0.1%
23.84 148
 
< 0.1%
24.05 148
 
< 0.1%
30.72 136
 
< 0.1%
30.19 136
 
< 0.1%
29.93 134
 
< 0.1%
16.53 134
 
< 0.1%
23.44 134
 
< 0.1%
23.64 134
 
< 0.1%
30.45 134
 
< 0.1%
Other values (5523) 33401
 
0.3%
(Missing) 12653504
99.7%
ValueCountFrequency (%)
0 6302
< 0.1%
0.01 24
 
< 0.1%
10 6
 
< 0.1%
11.11 26
 
< 0.1%
12 12
 
< 0.1%
14 6
 
< 0.1%
15 6
 
< 0.1%
15.61 67
 
< 0.1%
16.53 134
 
< 0.1%
17 6
 
< 0.1%
ValueCountFrequency (%)
182.4 1
< 0.1%
181.86 1
< 0.1%
153.62 1
< 0.1%
151.67 2
< 0.1%
151.23 2
< 0.1%
145.92 1
< 0.1%
145.49 1
< 0.1%
138.15 1
< 0.1%
137.72 1
< 0.1%
136.95 1
< 0.1%

PrimarySubscriberAndOneDependent
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5974
Distinct (%)14.6%
Missing12653504
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean49.835901
Minimum0
Maximum169.65
Zeros1848
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:40.312979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.74
Q127.39
median51.24
Q372.23
95-th percentile95.03
Maximum169.65
Range169.65
Interquartile range (IQR)44.84

Descriptive statistics

Standard deviation29.691985
Coefficient of variation (CV)0.59579509
Kurtosis-0.56205208
Mean49.835901
Median Absolute Deviation (MAD)22.22
Skewness0.11121116
Sum2040331.6
Variance881.61398
MonotonicityNot monotonic
2023-03-01T17:55:40.419851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1848
 
< 0.1%
7.35 1037
 
< 0.1%
4.41 929
 
< 0.1%
7.72 834
 
< 0.1%
4.63 798
 
< 0.1%
3.68 274
 
< 0.1%
0.74 262
 
< 0.1%
64.7 150
 
< 0.1%
23.84 148
 
< 0.1%
24.05 148
 
< 0.1%
Other values (5964) 34513
 
0.3%
(Missing) 12653504
99.7%
ValueCountFrequency (%)
0 1848
< 0.1%
0.74 262
 
< 0.1%
3.68 274
 
< 0.1%
4.41 929
< 0.1%
4.59 75
 
< 0.1%
4.63 798
< 0.1%
4.82 75
 
< 0.1%
5.3 12
 
< 0.1%
6.5 12
 
< 0.1%
7.35 1037
< 0.1%
ValueCountFrequency (%)
169.65 1
 
< 0.1%
169.15 1
 
< 0.1%
159.14 3
 
< 0.1%
151.41 1
 
< 0.1%
150.97 1
 
< 0.1%
148.17 1
 
< 0.1%
146.58 14
< 0.1%
144.87 14
< 0.1%
143.68 3
 
< 0.1%
143.45 1
 
< 0.1%

PrimarySubscriberAndTwoDependents
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7047
Distinct (%)17.2%
Missing12653504
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean70.660012
Minimum0
Maximum251.74
Zeros1848
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:40.537385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.47
Q127.91
median74.68
Q3107.67
95-th percentile138.09
Maximum251.74
Range251.74
Interquartile range (IQR)79.76

Descriptive statistics

Standard deviation44.62013
Coefficient of variation (CV)0.63147641
Kurtosis-1.1272255
Mean70.660012
Median Absolute Deviation (MAD)41.77
Skewness0.059702428
Sum2892891.6
Variance1990.956
MonotonicityNot monotonic
2023-03-01T17:55:40.655272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1848
 
< 0.1%
8.82 929
 
< 0.1%
14.71 811
 
< 0.1%
9.26 798
 
< 0.1%
15.45 609
 
< 0.1%
7.35 274
 
< 0.1%
1.47 262
 
< 0.1%
14.7 225
 
< 0.1%
15.44 225
 
< 0.1%
128.25 154
 
< 0.1%
Other values (7037) 34806
 
0.3%
(Missing) 12653504
99.7%
ValueCountFrequency (%)
0 1848
< 0.1%
1.47 262
 
< 0.1%
5.3 12
 
< 0.1%
6.5 12
 
< 0.1%
7.35 274
 
< 0.1%
8.82 929
< 0.1%
9.18 75
 
< 0.1%
9.26 798
< 0.1%
9.64 75
 
< 0.1%
14.7 225
 
< 0.1%
ValueCountFrequency (%)
251.74 3
< 0.1%
248.11 1
 
< 0.1%
247.38 1
 
< 0.1%
226.99 1
 
< 0.1%
226.32 1
 
< 0.1%
220.83 3
< 0.1%
212.7 1
 
< 0.1%
208.96 1
 
< 0.1%
202.73 1
 
< 0.1%
202.04 1
 
< 0.1%

PrimarySubscriberAndThreeOrMoreDependents
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7632
Distinct (%)18.6%
Missing12653504
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean89.405494
Minimum0
Maximum357.94
Zeros1848
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:40.774254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.94
Q129.41
median98.8
Q3137.08
95-th percentile175.77
Maximum357.94
Range357.94
Interquartile range (IQR)107.67

Descriptive statistics

Standard deviation57.699279
Coefficient of variation (CV)0.64536614
Kurtosis-1.1698297
Mean89.405494
Median Absolute Deviation (MAD)54.8
Skewness0.065631563
Sum3660350.3
Variance3329.2068
MonotonicityNot monotonic
2023-03-01T17:55:40.885702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1848
 
< 0.1%
29.41 764
 
< 0.1%
17.65 656
 
< 0.1%
30.9 577
 
< 0.1%
18.52 525
 
< 0.1%
14.71 274
 
< 0.1%
22.05 273
 
< 0.1%
13.23 273
 
< 0.1%
23.16 273
 
< 0.1%
13.89 273
 
< 0.1%
Other values (7622) 35205
 
0.3%
(Missing) 12653504
99.7%
ValueCountFrequency (%)
0 1848
< 0.1%
2.94 262
 
< 0.1%
5.3 12
 
< 0.1%
6.5 12
 
< 0.1%
13.23 273
 
< 0.1%
13.76 24
 
< 0.1%
13.77 51
 
< 0.1%
13.89 273
 
< 0.1%
14.45 24
 
< 0.1%
14.46 51
 
< 0.1%
ValueCountFrequency (%)
357.94 1
 
< 0.1%
356.89 1
 
< 0.1%
344.34 3
< 0.1%
332.8 1
 
< 0.1%
331.82 1
 
< 0.1%
301.47 1
 
< 0.1%
297.99 3
< 0.1%
286.35 1
 
< 0.1%
285.51 1
 
< 0.1%
282.85 1
 
< 0.1%

CoupleAndOneDependent
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7268
Distinct (%)17.8%
Missing12653504
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean72.782793
Minimum0
Maximum260.85
Zeros1848
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:41.005170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.74
Q128.26
median77.25
Q3110.43
95-th percentile146.28
Maximum260.85
Range260.85
Interquartile range (IQR)82.17

Descriptive statistics

Standard deviation48.232537
Coefficient of variation (CV)0.66269148
Kurtosis-0.88702674
Mean72.782793
Median Absolute Deviation (MAD)43.86
Skewness0.13836831
Sum2979800.3
Variance2326.3776
MonotonicityNot monotonic
2023-03-01T17:55:41.139028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1848
 
< 0.1%
7.35 1037
 
< 0.1%
4.41 929
 
< 0.1%
7.72 834
 
< 0.1%
4.63 798
 
< 0.1%
3.68 274
 
< 0.1%
0.74 262
 
< 0.1%
24.05 148
 
< 0.1%
23.84 148
 
< 0.1%
91.5 136
 
< 0.1%
Other values (7258) 34527
 
0.3%
(Missing) 12653504
99.7%
ValueCountFrequency (%)
0 1848
< 0.1%
0.74 262
 
< 0.1%
3.68 274
 
< 0.1%
4.41 929
< 0.1%
4.59 75
 
< 0.1%
4.63 798
< 0.1%
4.82 75
 
< 0.1%
7.1 12
 
< 0.1%
7.35 1037
< 0.1%
7.64 75
 
< 0.1%
ValueCountFrequency (%)
260.85 1
 
< 0.1%
260.08 1
 
< 0.1%
227.25 1
 
< 0.1%
226.58 1
 
< 0.1%
225.67 3
 
< 0.1%
222.79 14
< 0.1%
220.19 14
< 0.1%
219.69 1
 
< 0.1%
217.63 14
< 0.1%
216.3 1
 
< 0.1%

CoupleAndTwoDependents
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7733
Distinct (%)18.9%
Missing12653504
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean89.012859
Minimum0
Maximum339.31
Zeros1848
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:41.255880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.47
Q128.26
median97.91
Q3136.33
95-th percentile179.74
Maximum339.31
Range339.31
Interquartile range (IQR)108.07

Descriptive statistics

Standard deviation59.139094
Coefficient of variation (CV)0.66438821
Kurtosis-1.1492624
Mean89.012859
Median Absolute Deviation (MAD)55.23
Skewness0.058426912
Sum3644275.4
Variance3497.4324
MonotonicityNot monotonic
2023-03-01T17:55:41.381365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1848
 
< 0.1%
8.82 929
 
< 0.1%
14.71 812
 
< 0.1%
9.26 798
 
< 0.1%
15.45 609
 
< 0.1%
7.35 274
 
< 0.1%
1.47 262
 
< 0.1%
14.7 225
 
< 0.1%
15.44 225
 
< 0.1%
23.84 148
 
< 0.1%
Other values (7723) 34811
 
0.3%
(Missing) 12653504
99.7%
ValueCountFrequency (%)
0 1848
< 0.1%
1.47 262
 
< 0.1%
7.1 12
 
< 0.1%
7.35 274
 
< 0.1%
8.7 12
 
< 0.1%
8.82 929
< 0.1%
9.18 75
 
< 0.1%
9.26 798
< 0.1%
9.64 75
 
< 0.1%
14.7 225
 
< 0.1%
ValueCountFrequency (%)
339.31 1
 
< 0.1%
338.31 1
 
< 0.1%
318.27 3
< 0.1%
302.83 1
 
< 0.1%
301.94 1
 
< 0.1%
287.37 3
< 0.1%
285.77 1
 
< 0.1%
273.44 1
 
< 0.1%
272.64 1
 
< 0.1%
271.45 1
 
< 0.1%

CoupleAndThreeOrMoreDependents
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8197
Distinct (%)20.0%
Missing12653504
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean107.80777
Minimum0
Maximum449.14
Zeros1848
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:41.505386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.94
Q129.49
median116.71
Q3167.96
95-th percentile220.02
Maximum449.14
Range449.14
Interquartile range (IQR)138.47

Descriptive statistics

Standard deviation73.241174
Coefficient of variation (CV)0.6793682
Kurtosis-1.1498462
Mean107.80777
Median Absolute Deviation (MAD)72.59
Skewness0.11742448
Sum4413758.1
Variance5364.2695
MonotonicityNot monotonic
2023-03-01T17:55:41.634052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1848
 
< 0.1%
29.41 764
 
< 0.1%
17.65 656
 
< 0.1%
30.9 577
 
< 0.1%
18.52 525
 
< 0.1%
14.71 274
 
< 0.1%
22.05 273
 
< 0.1%
13.23 273
 
< 0.1%
23.16 273
 
< 0.1%
13.89 273
 
< 0.1%
Other values (8187) 35205
 
0.3%
(Missing) 12653504
99.7%
ValueCountFrequency (%)
0 1848
< 0.1%
2.94 262
 
< 0.1%
7.1 12
 
< 0.1%
8.7 12
 
< 0.1%
13.23 273
 
< 0.1%
13.76 24
 
< 0.1%
13.77 51
 
< 0.1%
13.89 273
 
< 0.1%
14.45 24
 
< 0.1%
14.46 51
 
< 0.1%
ValueCountFrequency (%)
449.14 1
 
< 0.1%
447.82 1
 
< 0.1%
410.88 3
< 0.1%
408.64 1
 
< 0.1%
407.43 1
 
< 0.1%
378.27 1
 
< 0.1%
364.52 3
< 0.1%
359.31 1
 
< 0.1%
358.68 1
 
< 0.1%
358.26 1
 
< 0.1%

RowNumber
Real number (ℝ)

Distinct60444
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6348.5724
Minimum14
Maximum63493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.9 MiB
2023-03-01T17:55:41.768302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile131
Q1873
median2728
Q37577
95-th percentile26079
Maximum63493
Range63479
Interquartile range (IQR)6704

Descriptive statistics

Standard deviation9011.435
Coefficient of variation (CV)1.4194427
Kurtosis6.6476121
Mean6348.5724
Median Absolute Deviation (MAD)2279
Skewness2.4522055
Sum8.0591603 × 1010
Variance81205960
MonotonicityNot monotonic
2023-03-01T17:55:41.874891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 6266
 
< 0.1%
15 6263
 
< 0.1%
16 6242
 
< 0.1%
17 6221
 
< 0.1%
18 6185
 
< 0.1%
19 6173
 
< 0.1%
20 6139
 
< 0.1%
21 6113
 
< 0.1%
22 6041
 
< 0.1%
23 6033
 
< 0.1%
Other values (60434) 12632769
99.5%
ValueCountFrequency (%)
14 6266
< 0.1%
15 6263
< 0.1%
16 6242
< 0.1%
17 6221
< 0.1%
18 6185
< 0.1%
19 6173
< 0.1%
20 6139
< 0.1%
21 6113
< 0.1%
22 6041
< 0.1%
23 6033
< 0.1%
ValueCountFrequency (%)
63493 4
< 0.1%
63492 4
< 0.1%
63491 4
< 0.1%
63490 4
< 0.1%
63489 4
< 0.1%
63488 4
< 0.1%
63487 4
< 0.1%
63486 4
< 0.1%
63485 4
< 0.1%
63484 4
< 0.1%

Interactions

2023-03-01T17:52:03.225330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:23.349419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:29.372110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:35.039873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:40.571228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:46.002785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:49.382918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:51.271318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:53.155732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:55.002325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:57.040869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:58.872931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:00.721187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:04.103768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:24.349453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:30.202850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:35.877244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:41.392798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:46.462464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:49.518119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:51.407243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:53.293814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:55.148801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:57.173491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:59.008828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:00.850157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:04.956479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:25.186178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:31.043902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:36.676061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:42.216618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:47.057572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:49.664552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:51.552783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:53.426580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:55.298995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:57.312663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:59.144166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:00.982471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:05.811880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:26.050681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:31.913311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:37.493597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:43.003291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:47.494025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:49.808951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:51.706878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:53.582407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:55.594759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:57.456838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:59.290685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:01.125142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:06.255801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:26.638484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:32.354901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:37.925365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:43.454648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:47.906001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:49.951122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:51.843865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:53.721967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:55.737317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:57.596703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:59.427083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:01.258093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:06.402484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:26.786394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:32.494026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:38.069395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:43.598956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:48.047872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:50.062541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:51.970190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:53.859324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:55.862780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:57.724235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:59.552705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:01.382447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:06.688323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:26.936440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:32.631695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:38.206477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:43.744209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:48.179195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:50.221026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:52.101003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:54.013356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:56.015252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:57.873470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:59.708988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:01.530479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:06.828824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:27.076017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:32.772072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:38.344578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:43.889946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:48.308488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:50.381495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:52.256633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:54.138729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:56.166793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:58.027493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:59.861897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:01.678745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:06.965041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:27.225895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:32.903817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:38.473685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:44.031809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:48.430560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:50.533890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:52.404169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:54.282610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:56.294769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:58.172724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:00.007868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:01.821644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:07.107898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:27.365432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:33.045010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:38.621757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:44.182266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:48.552636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:50.687432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:52.561051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:54.431402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:56.445354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:58.304753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:00.162634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:01.971627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:07.242748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:27.512989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:33.187065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:38.757678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:44.320197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:48.672997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:50.835086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:52.711594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:54.579701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:56.592802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:58.446570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:00.287433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:02.114430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:07.384830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:27.650753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:33.326472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:38.893403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:44.465859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:48.792789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:50.986946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:52.865216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:54.726928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:56.749223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:58.595234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:00.442286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:02.240571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:08.208269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:28.499502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:34.180829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:39.727147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:45.356261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:49.224421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:51.124813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:53.003098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:54.860033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:56.890044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:51:58.726651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:00.577004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-01T17:52:02.367473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-01T17:55:41.998019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
IssuerIdVersionNumIssuerId2IndividualRateIndividualTobaccoRateCouplePrimarySubscriberAndOneDependentPrimarySubscriberAndTwoDependentsPrimarySubscriberAndThreeOrMoreDependentsCoupleAndOneDependentCoupleAndTwoDependentsCoupleAndThreeOrMoreDependentsRowNumberBusinessYearStateCodeSourceNameRateEffectiveDateRateExpirationDateRatingAreaIdTobacco
IssuerId1.000-0.1101.000-0.0750.0240.001-0.005-0.0020.001-0.004-0.011-0.006-0.0590.0920.3540.2120.0630.0910.1330.308
VersionNum-0.1101.000-0.1100.3240.031-0.123-0.116-0.135-0.110-0.105-0.106-0.0830.1710.2450.2720.1460.1440.1490.1020.327
IssuerId21.000-0.1101.000-0.0750.0240.001-0.005-0.0020.001-0.004-0.011-0.006-0.0590.0920.3540.2120.0630.0910.1330.308
IndividualRate-0.0750.324-0.0751.0000.9770.7690.7060.6960.6550.7010.7060.6460.0870.0940.0570.0080.1730.1290.0160.049
IndividualTobaccoRate0.0240.0310.0240.9771.000NaNNaNNaNNaNNaNNaNNaN0.0770.0460.0900.0600.0460.0430.0270.010
Couple0.001-0.1230.0010.769NaN1.0000.9720.9670.9390.9760.9800.938-0.2410.2670.1750.1900.2440.2430.0551.000
PrimarySubscriberAndOneDependent-0.005-0.116-0.0050.706NaN0.9721.0000.9780.9360.9940.9790.927-0.2180.2930.1980.2180.2310.2310.0551.000
PrimarySubscriberAndTwoDependents-0.002-0.135-0.0020.696NaN0.9670.9781.0000.9600.9820.9740.930-0.1950.3060.1780.1640.2050.2050.0491.000
PrimarySubscriberAndThreeOrMoreDependents0.001-0.1100.0010.655NaN0.9390.9360.9601.0000.9280.9740.985-0.1870.2480.1850.1490.1620.1640.0361.000
CoupleAndOneDependent-0.004-0.105-0.0040.701NaN0.9760.9940.9820.9281.0000.9750.915-0.2090.2970.1940.2240.2080.2060.0571.000
CoupleAndTwoDependents-0.011-0.106-0.0110.706NaN0.9800.9790.9740.9740.9751.0000.977-0.2030.2520.1880.1880.1830.1830.0541.000
CoupleAndThreeOrMoreDependents-0.006-0.083-0.0060.646NaN0.9380.9270.9300.9850.9150.9771.000-0.1870.2370.1800.1370.1730.1730.0411.000
RowNumber-0.0590.171-0.0590.0870.077-0.241-0.218-0.195-0.187-0.209-0.203-0.1871.0000.0680.1750.1170.0550.0570.1280.216
BusinessYear0.0920.2450.0920.0940.0460.2670.2930.3060.2480.2970.2520.2370.0681.0000.1870.0201.0000.9900.0620.064
StateCode0.3540.2720.3540.0570.0900.1750.1980.1780.1850.1940.1880.1800.1750.1871.0000.7190.1140.1190.1900.387
SourceName0.2120.1460.2120.0080.0600.1900.2180.1640.1490.2240.1880.1370.1170.0200.7191.0000.0550.1030.2450.090
RateEffectiveDate0.0630.1440.0630.1730.0460.2440.2310.2050.1620.2080.1830.1730.0551.0000.1140.0551.0000.7720.0450.113
RateExpirationDate0.0910.1490.0910.1290.0430.2430.2310.2050.1640.2060.1830.1730.0570.9900.1190.1030.7721.0000.0380.161
RatingAreaId0.1330.1020.1330.0160.0270.0550.0550.0490.0360.0570.0540.0410.1280.0620.1900.2450.0450.0381.0000.147
Tobacco0.3080.3270.3080.0490.0101.0001.0001.0001.0001.0001.0001.0000.2160.0640.3870.0900.1130.1610.1471.000

Missing values

2023-03-01T17:52:28.574995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-01T17:53:00.110562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-01T17:55:04.250134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

BusinessYearStateCodeIssuerIdSourceNameVersionNumImportDateIssuerId2FederalTINRateEffectiveDateRateExpirationDatePlanIdRatingAreaIdTobaccoAgeIndividualRateIndividualTobaccoRateCouplePrimarySubscriberAndOneDependentPrimarySubscriberAndTwoDependentsPrimarySubscriberAndThreeOrMoreDependentsCoupleAndOneDependentCoupleAndTwoDependentsCoupleAndThreeOrMoreDependentsRowNumber
02014AK21989HIOS62014-03-19 07:06:492198993-04387722014-01-012014-12-3121989AK0010001Rating Area 1No Preference0-2029.00NaNNaNNaNNaNNaNNaNNaNNaN14
12014AK21989HIOS62014-03-19 07:06:492198993-04387722014-01-012014-12-3121989AK0020001Rating Area 1No PreferenceFamily Option36.95NaN73.9107.61107.61107.61144.56144.56144.5614
22014AK21989HIOS62014-03-19 07:06:492198993-04387722014-01-012014-12-3121989AK0020001Rating Area 2No PreferenceFamily Option36.95NaN73.9107.61107.61107.61144.56144.56144.5615
32014AK21989HIOS62014-03-19 07:06:492198993-04387722014-01-012014-12-3121989AK0010001Rating Area 1No Preference2132.00NaNNaNNaNNaNNaNNaNNaNNaN15
42014AK21989HIOS62014-03-19 07:06:492198993-04387722014-01-012014-12-3121989AK0010001Rating Area 1No Preference2232.00NaNNaNNaNNaNNaNNaNNaNNaN16
52014AK21989HIOS62014-03-19 07:06:492198993-04387722014-01-012014-12-3121989AK0020001Rating Area 3No PreferenceFamily Option36.95NaN73.9107.61107.61107.61144.56144.56144.5616
62014AK21989HIOS62014-03-19 07:06:492198993-04387722014-01-012014-12-3121989AK0020002Rating Area 1No PreferenceFamily Option32.45NaN64.994.5094.5094.50126.95126.95126.9517
72014AK21989HIOS62014-03-19 07:06:492198993-04387722014-01-012014-12-3121989AK0010001Rating Area 1No Preference2332.00NaNNaNNaNNaNNaNNaNNaNNaN17
82014AK21989HIOS62014-03-19 07:06:492198993-04387722014-01-012014-12-3121989AK0010001Rating Area 1No Preference2432.00NaNNaNNaNNaNNaNNaNNaNNaN18
92014AK21989HIOS62014-03-19 07:06:492198993-04387722014-01-012014-12-3121989AK0020002Rating Area 2No PreferenceFamily Option32.45NaN64.994.5094.5094.50126.95126.95126.9518
BusinessYearStateCodeIssuerIdSourceNameVersionNumImportDateIssuerId2FederalTINRateEffectiveDateRateExpirationDatePlanIdRatingAreaIdTobaccoAgeIndividualRateIndividualTobaccoRateCouplePrimarySubscriberAndOneDependentPrimarySubscriberAndTwoDependentsPrimarySubscriberAndThreeOrMoreDependentsCoupleAndOneDependentCoupleAndTwoDependentsCoupleAndThreeOrMoreDependentsRowNumber
126944352016WV96480SERFF22015-08-20 12:28:369648013-51233902016-01-012016-12-3196480WV0090003Rating Area 11No Preference5614.05NaNNaNNaNNaNNaNNaNNaNNaN2028
126944362016WV96480SERFF22015-08-20 12:28:369648013-51233902016-01-012016-12-3196480WV0090003Rating Area 11No Preference5714.05NaNNaNNaNNaNNaNNaNNaNNaN2029
126944372016WV96480SERFF22015-08-20 12:28:369648013-51233902016-01-012016-12-3196480WV0090003Rating Area 11No Preference5814.05NaNNaNNaNNaNNaNNaNNaNNaN2030
126944382016WV96480SERFF22015-08-20 12:28:369648013-51233902016-01-012016-12-3196480WV0090003Rating Area 11No Preference5914.05NaNNaNNaNNaNNaNNaNNaNNaN2031
126944392016WV96480SERFF22015-08-20 12:28:369648013-51233902016-01-012016-12-3196480WV0090003Rating Area 11No Preference6014.05NaNNaNNaNNaNNaNNaNNaNNaN2032
126944402016WV96480SERFF22015-08-20 12:28:369648013-51233902016-01-012016-12-3196480WV0090003Rating Area 11No Preference6114.05NaNNaNNaNNaNNaNNaNNaNNaN2033
126944412016WV96480SERFF22015-08-20 12:28:369648013-51233902016-01-012016-12-3196480WV0090003Rating Area 11No Preference6214.05NaNNaNNaNNaNNaNNaNNaNNaN2034
126944422016WV96480SERFF22015-08-20 12:28:369648013-51233902016-01-012016-12-3196480WV0090003Rating Area 11No Preference6314.05NaNNaNNaNNaNNaNNaNNaNNaN2035
126944432016WV96480SERFF22015-08-20 12:28:369648013-51233902016-01-012016-12-3196480WV0090003Rating Area 11No Preference6414.05NaNNaNNaNNaNNaNNaNNaNNaN2036
126944442016WV96480SERFF22015-08-20 12:28:369648013-51233902016-01-012016-12-3196480WV0090003Rating Area 11No Preference65 and over14.05NaNNaNNaNNaNNaNNaNNaNNaN2037

Duplicate rows

Most frequently occurring

BusinessYearStateCodeIssuerIdSourceNameVersionNumImportDateIssuerId2FederalTINRateEffectiveDateRateExpirationDatePlanIdRatingAreaIdTobaccoIndividualRateIndividualTobaccoRateCouplePrimarySubscriberAndOneDependentPrimarySubscriberAndTwoDependentsPrimarySubscriberAndThreeOrMoreDependentsCoupleAndOneDependentCoupleAndTwoDependentsCoupleAndThreeOrMoreDependentsRowNumber# duplicates
02014AL82285HIOS72014-01-21 08:29:498228594-27615372014-01-012014-12-3182285AL0010001Rating Area 1No Preference19.42NaNNaNNaNNaNNaNNaNNaNNaN1522
12014AL82285HIOS72014-01-21 08:29:498228594-27615372014-01-012014-12-3182285AL0010001Rating Area 1No Preference19.42NaNNaNNaNNaNNaNNaNNaNNaN1532
22014AL82285HIOS72014-01-21 08:29:498228594-27615372014-01-012014-12-3182285AL0010001Rating Area 1No Preference19.42NaNNaNNaNNaNNaNNaNNaNNaN1542
32014AL82285HIOS72014-01-21 08:29:498228594-27615372014-01-012014-12-3182285AL0010001Rating Area 1No Preference19.42NaNNaNNaNNaNNaNNaNNaNNaN1552
42014AL82285HIOS72014-01-21 08:29:498228594-27615372014-01-012014-12-3182285AL0010001Rating Area 1No Preference19.42NaNNaNNaNNaNNaNNaNNaNNaN1562
52014AL82285HIOS72014-01-21 08:29:498228594-27615372014-01-012014-12-3182285AL0010001Rating Area 1No Preference19.42NaNNaNNaNNaNNaNNaNNaNNaN1572
62014AL82285HIOS72014-01-21 08:29:498228594-27615372014-01-012014-12-3182285AL0010001Rating Area 1No Preference19.42NaNNaNNaNNaNNaNNaNNaNNaN1582
72014AL82285HIOS72014-01-21 08:29:498228594-27615372014-01-012014-12-3182285AL0010001Rating Area 1No Preference19.42NaNNaNNaNNaNNaNNaNNaNNaN1592
82014AL82285HIOS72014-01-21 08:29:498228594-27615372014-01-012014-12-3182285AL0010001Rating Area 1No Preference19.42NaNNaNNaNNaNNaNNaNNaNNaN1602
92014AL82285HIOS72014-01-21 08:29:498228594-27615372014-01-012014-12-3182285AL0010001Rating Area 1No Preference19.42NaNNaNNaNNaNNaNNaNNaNNaN1612